Traffic safety is a critical factor for the sustainable development of urban transportation systems. This study investigates the impact of historical accident information on the prediction of future traffic accident risks, as well as the interaction between this information and other features, such as driver violations and vehicle attributes. Using a comprehensive dataset of traffic accidents involving passenger vehicles in a western Chinese city, we developed two predictive models: Model 1, which is based on vehicle information and driver violations, and Model 2, which integrates historical accident data. The results indicate that the inclusion of historical accident information significantly enhances the predictive performance of the model, particularly in terms of AUC (Area Under the Curve) and AP (Average Precision) values. Furthermore, through feature importance analysis and SHAP (SHapley Additive exPlanations) value evaluation, this study reveals the interaction effects between historical accident data and other features, and how these interactions influence model decisions. The findings suggest that historical accident data play a positive role in predicting future accident risk, with varying effects on risk mitigation. These insights provide a scientific basis for developing strategies to ensure the sustainable development of urban transportation systems.

Given the high casualty rate on expressways, this study aimed to accurately predict traffic accident rates and the key factors influencing them. Taking an expressway in Southern China as the research object, we constructed a two-layer stacking model integrating neural networks and tree models, based on accident data, traffic flow data, and road segment characteristic data. Six base models were integrated for prediction, and the Shapley Additive exPlanations (SHAP) method was used to analyze influencing factors. Results showed that the proposed model achieved the best performance, with a root mean square error (RMSE) of 11.05 and a mean absolute error (MAE) of 6.12, and its performance was significantly superior to that of other models (p < 0.05). Results from hyperparameter optimization and 5-fold cross-validation indicated that the proposed model had an RMSE of 8.91 ± 2.03, which was better than that of other models. Among all input factors, the proportion of tunnel length to total length and the variance of bridge width had the most significant impact on the expressway traffic accident rate, while the average width of tunnels had the lowest impact. This study realizes accurate prediction using widely available data and clarifies key factor mechanisms, providing support for expressway safety management and early risk warnings.

The driving safety field (DSF) model, which comprehensively evaluates driving risks in complex environments by integrating human–vehicle–road factors, serves as a quantitative methodology for assessing dynamic traffic risks. However, it exhibits limitations in certain scenarios where its risk characterization deviates from actual risk variations. To address the challenges, an improved driving safety field (IDSF) model is proposed. The new framework redesigns the calculation of virtual mass, field force, and driving safety index. Parameters in the model were calibrated using accident data and driving simulator experiments. Results demonstrate that the IDSF outperforms conventional time‐to‐collision (TTC) inverse (TTCi) and DSF models. Specifically, in car‐following scenarios, IDSF demonstrates higher correlation ( r ≈0.9) with the TTCi model. In complex environments with high vehicular heterogeneity, compared with the DSF model, the IDSF model exhibits greater stability (80% lower coefficient of variation) and fewer extreme deviations (38% reduction). This study provides a novel theoretical framework for automotive intelligent safety technologies and offers valuable insights for designing more reasonable driving safety algorithms.